AIM:To investigate the prevalence of,and risk factors for,diabetes mellitus(DM) in Algerian patients with chronic hepatitis C virus(HCV) infection and in a control group.METHODS:A cross-sectional study was undertaken....AIM:To investigate the prevalence of,and risk factors for,diabetes mellitus(DM) in Algerian patients with chronic hepatitis C virus(HCV) infection and in a control group.METHODS:A cross-sectional study was undertaken.A total of 416 consecutive patients with viral chronic hepatitis attending the Internal Medicine Department of the University Hospital Center Touhami Benflis in Batna [290 HCV-infected and 126 hepatitis B virus(HBV)-infected patients] were prospectively recruited.RESULTS:The prevalence of DM was higher in HCV-infected patients in comparison with HBV-infected patients(39.1% vs 5%,P < 0.0001).Among patients without cirrhosis,diabetes was more prevalent in HCV-infected patients than in HBV-infected patients(33.5% vs 4.3%,P < 0.0001).Among patients with cirrhosis,diabetes was more prevalent in HCV-infected patients,but the difference was not significant(67.4% vs 20%,P = 0.058).The logistic regression analysis showed that HCV infection [odds ratio(OR) 4.73,95% CI:1.7-13.2],metabolic syndrome(OR 12.35,95% CI:6.18-24.67),family history of diabetes(OR 3.2,95% CI:1.67-6.13) and increased hepatic enzymes(OR 2.22,95% CI:1.1-4.5) were independently related to DM in these patients.CONCLUSION:The high prevalence of diabetes in HCV-infected patients,and its occurrence at early stages of hepatic disease,suggest that screening for glucose abnormalities should be indicated in these patients.展开更多
Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/o...Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.展开更多
Saline wetlands are rare ecosystems in Saharan areas, which are important for conservation of many endemic and rare plant species. In this study, we investigated five saline wetland sites of the Oued Righ region, loca...Saline wetlands are rare ecosystems in Saharan areas, which are important for conservation of many endemic and rare plant species. In this study, we investigated five saline wetland sites of the Oued Righ region, located in the northeastern Algeria, to determine the environmental factors controlling the composition and distribution of plant communities. We established a total of 20 transects to measure the vegetation parameters （density and cover） and soil characteristics （electrical conductivity, moisture, pH, CaSO4, CaCO3, organic matter, Na＋, K＋, Mg2＋, Ca2＋, SO42 , CI-, NO3- and HCO3-）. A total of 17 plant species belonging to seven families were identified. The natural vegetation was composed of halophytic and hydro-halophytic plant communities, presented specially by the species of Amaranthaceae family. Soils in the studied wetlands were moist, gypsiferous, alkaline, salty to very salty with dominance of chloride and calcium. Results of the Canonical Correspondence Analysis （CCA） showed that community structure and species distribution patterns of vegetation were mainly dependent on soil characteristics, mainly being soil salinity （CaSO4, K＋, Ca2＋ and CI-） and moisture. The distribution of plant species was found to follow a specific zonal pattern. Halocnemum strobilaceum was observed to grow in highly salt-affected soils, thus being the more salt-tolerant species. Phragmites communis plants were widely distributed in the study area with a high density at the edges of accumulated water body. Juncus maritimus, Tamarix gallica and Saficornia fructicosa grew in soils that are partially or completely flooded in winter. Suaeda fructicosa, Traganum nudatum, Arthrocnemum glaucum, Aeluropus littoralis, Cressa cretica and Cynodon dactylon were distributed in salty and moist soils away from the open water body. Plants of Zygophyllum album, Limonastrirum guyonianum, Cornulaca monacantha, Cistanche tinctoria, Mollugo nudicaulis and Sonchus maritimus were found in soils with less salty and moi展开更多
文摘AIM:To investigate the prevalence of,and risk factors for,diabetes mellitus(DM) in Algerian patients with chronic hepatitis C virus(HCV) infection and in a control group.METHODS:A cross-sectional study was undertaken.A total of 416 consecutive patients with viral chronic hepatitis attending the Internal Medicine Department of the University Hospital Center Touhami Benflis in Batna [290 HCV-infected and 126 hepatitis B virus(HBV)-infected patients] were prospectively recruited.RESULTS:The prevalence of DM was higher in HCV-infected patients in comparison with HBV-infected patients(39.1% vs 5%,P < 0.0001).Among patients without cirrhosis,diabetes was more prevalent in HCV-infected patients than in HBV-infected patients(33.5% vs 4.3%,P < 0.0001).Among patients with cirrhosis,diabetes was more prevalent in HCV-infected patients,but the difference was not significant(67.4% vs 20%,P = 0.058).The logistic regression analysis showed that HCV infection [odds ratio(OR) 4.73,95% CI:1.7-13.2],metabolic syndrome(OR 12.35,95% CI:6.18-24.67),family history of diabetes(OR 3.2,95% CI:1.67-6.13) and increased hepatic enzymes(OR 2.22,95% CI:1.1-4.5) were independently related to DM in these patients.CONCLUSION:The high prevalence of diabetes in HCV-infected patients,and its occurrence at early stages of hepatic disease,suggest that screening for glucose abnormalities should be indicated in these patients.
文摘Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.
基金the Saharan Bio-resources Laboratory,Safeguarding and Valorization,Kasdi Merbah University of Ouargla,Algeria for providing support for this research
文摘Saline wetlands are rare ecosystems in Saharan areas, which are important for conservation of many endemic and rare plant species. In this study, we investigated five saline wetland sites of the Oued Righ region, located in the northeastern Algeria, to determine the environmental factors controlling the composition and distribution of plant communities. We established a total of 20 transects to measure the vegetation parameters （density and cover） and soil characteristics （electrical conductivity, moisture, pH, CaSO4, CaCO3, organic matter, Na＋, K＋, Mg2＋, Ca2＋, SO42 , CI-, NO3- and HCO3-）. A total of 17 plant species belonging to seven families were identified. The natural vegetation was composed of halophytic and hydro-halophytic plant communities, presented specially by the species of Amaranthaceae family. Soils in the studied wetlands were moist, gypsiferous, alkaline, salty to very salty with dominance of chloride and calcium. Results of the Canonical Correspondence Analysis （CCA） showed that community structure and species distribution patterns of vegetation were mainly dependent on soil characteristics, mainly being soil salinity （CaSO4, K＋, Ca2＋ and CI-） and moisture. The distribution of plant species was found to follow a specific zonal pattern. Halocnemum strobilaceum was observed to grow in highly salt-affected soils, thus being the more salt-tolerant species. Phragmites communis plants were widely distributed in the study area with a high density at the edges of accumulated water body. Juncus maritimus, Tamarix gallica and Saficornia fructicosa grew in soils that are partially or completely flooded in winter. Suaeda fructicosa, Traganum nudatum, Arthrocnemum glaucum, Aeluropus littoralis, Cressa cretica and Cynodon dactylon were distributed in salty and moist soils away from the open water body. Plants of Zygophyllum album, Limonastrirum guyonianum, Cornulaca monacantha, Cistanche tinctoria, Mollugo nudicaulis and Sonchus maritimus were found in soils with less salty and moi
